notebooks/06t_01_similarity_matching.ipynb

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    {
      "cell_type": "markdown",
      "source": [
        "# Similarity Matching\n",
        "\n",
        "อ.ดร.ธนชาตย์ ฤทธิ์บำรุง (อ.เชค)\n",
        "\n",
        "[![Open In Colab](https://colab.research.google.com/assets/colab-badge.svg)](https://colab.research.google.com/github/ai-builders/curriculum/blob/main/notebooks/06t_01_similarity_matching.ipynb)\n"
      ],
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        "id": "VqFcNhYRAwzZ"
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    {
      "cell_type": "markdown",
      "source": [
        "**เป้าหมายการเรียนรู้**\n",
        "1. เข้าใจถึงการใช้ Similarity Index และ Distance ในการวัดความเหมือนของสองสิ่ง\n",
        "2. สามารถเลือกใช้ Similarity Index และ Distance ที่เหมาะสมได้\n",
        "3. สามารถประยุกต์ใช้ Similarity Index และ Distance กับโจทย์ต่างๆ ได้"
      ],
      "metadata": {
        "id": "0kigs1orA3Px"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "# How do we measure similarity?"
      ],
      "metadata": {
        "id": "W3a7dQH_33Yz"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "เรารู้ได้ยังไงว่าใครเหมือนกัน แล้วจะวัดออกมาเป็นค่าได้อย่างไร?\n",
        "\n",
        "อย่างในกรณีด้านล่าง คู่ใด มีความเหมือนกันมากที่สุด?\n",
        "\n",
        "*   Jenny - Mary\n",
        "*   Jenny - Bobby\n",
        "*   Mary - Bobby"
      ],
      "metadata": {
        "id": "0PMq0CijHbCq"
      }
    },
    {
      "cell_type": "markdown",
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        "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/48.png width=\"750px\">"
      ],
      "metadata": {
        "id": "lWb3bOArGXl9"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "## Similarity Index"
      ],
      "metadata": {
        "id": "9S7ZdGBNIv5k"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   Similarity Index เป็นเทคนิคในการวัดความเหมือนระหว่างของสองสิ่ง เช่น ความเหมือนของคน ความเหมือนของเอกสาร เป็นต้น\n",
        "*   มีสูตรหลากหลายในการวัด Similarity Index"
      ],
      "metadata": {
        "id": "znMLUfyuJKwy"
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    },
    {
      "cell_type": "markdown",
      "source": [
        "### Jaccard Similarity\n",
        "\n",
        "$$ Jaccard(U,V) = \\frac{|U \\cap V|}{|U \\cup V|} $$"
      ],
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      "source": [
        "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/49.jpg width=\"750px\">\n",
        "\n",
        "cr: https://medium.com/data-cafe-thailand/%E0%B9%80%E0%B8%A1%E0%B8%B7%E0%B9%88%E0%B8%AD%E0%B8%AA%E0%B8%B2%E0%B8%A2-data-%E0%B8%AD%E0%B8%A2%E0%B8%B2%E0%B8%81%E0%B8%88%E0%B8%B0%E0%B8%81%E0%B8%B4%E0%B8%99-pizza-%E0%B9%82%E0%B8%94%E0%B8%A2%E0%B9%83%E0%B8%8A%E0%B9%89-jaccard-similarity-%E0%B9%81%E0%B8%A5%E0%B8%B0-cosine-similarity-f921fa4ab043"
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      "source": [
        "Jenny = ['Shabu Shabu', 'Sushi', 'Somtam']\n",
        "Mary = ['Shabu Shabu', 'Sushi', 'Krapow']\n",
        "Bobby = ['Ramen', 'Moo Krata', 'Krapow']"
      ],
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      "source": [
        "def jaccard_similarity(list1, list2):\n",
        "    intersection = len(list(set(list1).intersection(list2)))\n",
        "    union = (len(set(list1)) + len(set(list2))) - intersection\n",
        "    return float(intersection) / union"
      ],
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      "source": [
        "print('Jenny - Mary :' + str(jaccard_similarity(Jenny, Mary)))\n",
        "print('Jenny - Bobby :' + str(jaccard_similarity(Jenny, Bobby)))\n",
        "print('Mary - Bobby :' + str(jaccard_similarity(Mary, Bobby)))"
      ],
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      "execution_count": 3,
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          "text": [
            "Jenny - Mary :0.5\n",
            "Jenny - Bobby :0.0\n",
            "Mary - Bobby :0.2\n"
          ]
        }
      ]
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      "source": [
        "## Convert to boolean array\n",
        "\n",
        "Food = ['Shabu Shabu', 'Sushi', 'Somtam', 'Krapow', 'Ramen', 'Moo Krata']\n",
        "Jenny = [1, 1, 1, 0 , 0, 0,]\n",
        "Mary = [1, 1, 0, 1, 0, 0]\n",
        "Bobby = [0, 0, 0, 1, 1, 1]"
      ],
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      "source": [
        "import pandas as pd\n",
        "\n",
        "df = pd.DataFrame([Jenny, Mary, Bobby], columns=Food, index=['Jenny', 'Mary', 'Bobby'])\n",
        "df"
      ],
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              "       Shabu Shabu  Sushi  Somtam  Krapow  Ramen  Moo Krata\n",
              "Jenny            1      1       1       0      0          0\n",
              "Mary             1      1       0       1      0          0\n",
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      "cell_type": "code",
      "source": [
        "## Jaccard Similarity = 1 - Jaccard Distance\n",
        "\n",
        "from scipy.spatial import distance\n",
        "\n",
        "1 - distance.jaccard(df[df.index=='Jenny'], df[df.index=='Mary'])"
      ],
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        "colab": {
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          "data": {
            "text/plain": [
              "0.5"
            ]
          },
          "metadata": {},
          "execution_count": 6
        }
      ]
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      "cell_type": "code",
      "source": [
        "print('Jenny - Mary :' + str(1-distance.jaccard(df[df.index=='Jenny'], df[df.index=='Mary'])))\n",
        "print('Jenny - Bobby :' + str(1-distance.jaccard(df[df.index=='Jenny'], df[df.index=='Bobby'])))\n",
        "print('Mary - Bobby :' + str(1-distance.jaccard(df[df.index=='Mary'], df[df.index=='Bobby'])))"
      ],
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        "colab": {
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        "outputId": "b23be205-e45a-418b-fe62-3de42ec93601"
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      "execution_count": 7,
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          "text": [
            "Jenny - Mary :0.5\n",
            "Jenny - Bobby :0.0\n",
            "Mary - Bobby :0.19999999999999996\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Cosine Similarity"
      ],
      "metadata": {
        "id": "H67DJwHKEKPi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/50.jpg width=\"500px\">"
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      "metadata": {
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      "source": [
        "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/51.jpg width=\"750px\">\n",
        "\n",
        "cr: https://deepai.org/machine-learning-glossary-and-terms/cosine-similarity"
      ],
      "metadata": {
        "id": "-eEnvbmome4W"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "## Cosine Similarity = 1 - Cosine Distance\n",
        "\n",
        "from scipy.spatial import distance\n",
        "\n",
        "1 - distance.cosine(df[df.index=='Jenny'], df[df.index=='Mary'])"
      ],
      "metadata": {
        "id": "fKvfIUFRNgB5",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "cff91d0d-433e-4140-9c0d-f093d25bacd4"
      },
      "execution_count": 8,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "0.6666666666666666"
            ]
          },
          "metadata": {},
          "execution_count": 8
        }
      ]
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    {
      "cell_type": "code",
      "source": [
        "print('Jenny - Mary :' + str(1-distance.cosine(df[df.index=='Jenny'], df[df.index=='Mary'])))\n",
        "print('Jenny - Bobby :' + str(1-distance.cosine(df[df.index=='Jenny'], df[df.index=='Bobby'])))\n",
        "print('Mary - Bobby :' + str(1-distance.cosine(df[df.index=='Mary'], df[df.index=='Bobby'])))"
      ],
      "metadata": {
        "colab": {
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        "id": "CwwaYbYqHnNN",
        "outputId": "0ceac2a0-54a5-43b1-b4e6-6e69f260cbc3"
      },
      "execution_count": 9,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jenny - Mary :0.6666666666666666\n",
            "Jenny - Bobby :0.0\n",
            "Mary - Bobby :0.33333333333333326\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Euclidean Distance"
      ],
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      "source": [
        "Jenny = [3, 5, 4, 1 , 0, 1,]\n",
        "Mary = [5, 4, 3, 5, 2, 1]\n",
        "Bobby = [1, 2, 1, 5, 4, 5]\n",
        "\n",
        "df = pd.DataFrame([Jenny, Mary, Bobby], columns=Food, index=['Jenny', 'Mary', 'Bobby'])\n",
        "df"
      ],
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      "execution_count": 10,
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            "text/plain": [
              "       Shabu Shabu  Sushi  Somtam  Krapow  Ramen  Moo Krata\n",
              "Jenny            3      5       4       1      0          1\n",
              "Mary             5      4       3       5      2          1\n",
              "Bobby            1      2       1       5      4          5"
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              "      <td>1</td>\n",
              "      <td>2</td>\n",
              "      <td>1</td>\n",
              "      <td>5</td>\n",
              "      <td>4</td>\n",
              "      <td>5</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-81a25cd8-bfef-4a6b-978a-bfc05f31efd3')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
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              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
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              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-81a25cd8-bfef-4a6b-978a-bfc05f31efd3 button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-81a25cd8-bfef-4a6b-978a-bfc05f31efd3');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 10
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "distance.euclidean(df[df.index=='Jenny'], df[df.index=='Mary'])"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "fkHQvS_-Iqhv",
        "outputId": "a9d4149a-0f38-4491-e9d2-017c1ba209fe"
      },
      "execution_count": 11,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "5.0990195135927845"
            ]
          },
          "metadata": {},
          "execution_count": 11
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "print('Jenny - Mary :' + str(distance.euclidean(df[df.index=='Jenny'], df[df.index=='Mary'])))\n",
        "print('Jenny - Bobby :' + str(distance.euclidean(df[df.index=='Jenny'], df[df.index=='Bobby'])))\n",
        "print('Mary - Bobby :' + str(distance.euclidean(df[df.index=='Mary'], df[df.index=='Bobby'])))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "Ec92HXz4I3G1",
        "outputId": "153208a8-b222-4a3c-f834-9a7ec5349ec8"
      },
      "execution_count": 12,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Jenny - Mary :5.0990195135927845\n",
            "Jenny - Bobby :8.366600265340756\n",
            "Mary - Bobby :6.6332495807108\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "A = [5, 100]\n",
        "B = [10, 150]\n",
        "C = [10, 100]\n",
        "\n",
        "print('A - B :' + str(distance.euclidean(A,B)))\n",
        "print('A - C :' + str(distance.euclidean(A,C)))\n",
        "print('B - C :' + str(distance.euclidean(B,C)))"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "3dPKKOUeKkSa",
        "outputId": "560fc89f-9bef-443e-f469-6824837f5151"
      },
      "execution_count": 13,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "A - B :50.24937810560445\n",
            "A - C :5.0\n",
            "B - C :50.0\n"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Product Recommendation"
      ],
      "metadata": {
        "id": "CfShmzaVNQMj"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/53.png width=\"750px\">"
      ],
      "metadata": {
        "id": "RXZgGZhVq_UK"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Apiori - Market Basket"
      ],
      "metadata": {
        "id": "oAj3nRs1r4x-"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/54.png width=\"750px\">"
      ],
      "metadata": {
        "id": "hdLEZje-rNhi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/55.png width=\"750px\">"
      ],
      "metadata": {
        "id": "LPDJD4AwrTvv"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/56.png width=\"750px\">"
      ],
      "metadata": {
        "id": "fbiZUHcbrhBG"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/57.png width=\"750px\">"
      ],
      "metadata": {
        "id": "6mS-9I6GrtNd"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "\n",
        "url = 'https://github.com/ai-builders/curriculum/raw/main/data/BreadBasket_DMS.csv'\n",
        "\n",
        "df = pd.read_csv(url)"
      ],
      "metadata": {
        "id": "DN8wbFHXNPGM"
      },
      "execution_count": 14,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "df.head(5)"
      ],
      "metadata": {
        "id": "m0w0t7vqNWKT",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 206
        },
        "outputId": "b61b16ef-e7f7-4743-cdb1-4bfe6cbf9c2d"
      },
      "execution_count": 15,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         Date      Time  Transaction           Item\n",
              "0  2016-10-30  09:58:11            1          Bread\n",
              "1  2016-10-30  10:05:34            2   Scandinavian\n",
              "2  2016-10-30  10:05:34            2   Scandinavian\n",
              "3  2016-10-30  10:07:57            3  Hot chocolate\n",
              "4  2016-10-30  10:07:57            3            Jam"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-0f6405fe-4d6a-4642-95ed-186e89cd244b\">\n",
              "    <div class=\"colab-df-container\">\n",
              "      <div>\n",
              "<style scoped>\n",
              "    .dataframe tbody tr th:only-of-type {\n",
              "        vertical-align: middle;\n",
              "    }\n",
              "\n",
              "    .dataframe tbody tr th {\n",
              "        vertical-align: top;\n",
              "    }\n",
              "\n",
              "    .dataframe thead th {\n",
              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>Date</th>\n",
              "      <th>Time</th>\n",
              "      <th>Transaction</th>\n",
              "      <th>Item</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>2016-10-30</td>\n",
              "      <td>09:58:11</td>\n",
              "      <td>1</td>\n",
              "      <td>Bread</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>2016-10-30</td>\n",
              "      <td>10:05:34</td>\n",
              "      <td>2</td>\n",
              "      <td>Scandinavian</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>2016-10-30</td>\n",
              "      <td>10:05:34</td>\n",
              "      <td>2</td>\n",
              "      <td>Scandinavian</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>2016-10-30</td>\n",
              "      <td>10:07:57</td>\n",
              "      <td>3</td>\n",
              "      <td>Hot chocolate</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>2016-10-30</td>\n",
              "      <td>10:07:57</td>\n",
              "      <td>3</td>\n",
              "      <td>Jam</td>\n",
              "    </tr>\n",
              "  </tbody>\n",
              "</table>\n",
              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0f6405fe-4d6a-4642-95ed-186e89cd244b')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-0f6405fe-4d6a-4642-95ed-186e89cd244b button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-0f6405fe-4d6a-4642-95ed-186e89cd244b');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 15
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "missing = df.isnull().sum()\n",
        "print(missing)"
      ],
      "metadata": {
        "id": "G-Ep5EIwNYmD",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "4fe71522-2c07-4e96-986e-7f7d20c1fd94"
      },
      "execution_count": 16,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Date           0\n",
            "Time           0\n",
            "Transaction    0\n",
            "Item           0\n",
            "dtype: int64\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.Item.unique()"
      ],
      "metadata": {
        "id": "OO-zHoHcNamE",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "29902092-5fa1-41ce-ebbf-d40a4737ca8c"
      },
      "execution_count": 17,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "array(['Bread', 'Scandinavian', 'Hot chocolate', 'Jam', 'Cookies',\n",
              "       'Muffin', 'Coffee', 'Pastry', 'Medialuna', 'Tea', 'NONE',\n",
              "       'Tartine', 'Basket', 'Mineral water', 'Farm House', 'Fudge',\n",
              "       'Juice', \"Ella's Kitchen Pouches\", 'Victorian Sponge', 'Frittata',\n",
              "       'Hearty & Seasonal', 'Soup', 'Pick and Mix Bowls', 'Smoothies',\n",
              "       'Cake', 'Mighty Protein', 'Chicken sand', 'Coke',\n",
              "       'My-5 Fruit Shoot', 'Focaccia', 'Sandwich', 'Alfajores', 'Eggs',\n",
              "       'Brownie', 'Dulce de Leche', 'Honey', 'The BART', 'Granola',\n",
              "       'Fairy Doors', 'Empanadas', 'Keeping It Local', 'Art Tray',\n",
              "       'Bowl Nic Pitt', 'Bread Pudding', 'Adjustment', 'Truffles',\n",
              "       'Chimichurri Oil', 'Bacon', 'Spread', 'Kids biscuit', 'Siblings',\n",
              "       'Caramel bites', 'Jammie Dodgers', 'Tiffin', 'Olum & polenta',\n",
              "       'Polenta', 'The Nomad', 'Hack the stack', 'Bakewell',\n",
              "       'Lemon and coconut', 'Toast', 'Scone', 'Crepes', 'Vegan mincepie',\n",
              "       'Bare Popcorn', 'Muesli', 'Crisps', 'Pintxos', 'Gingerbread syrup',\n",
              "       'Panatone', 'Brioche and salami', 'Afternoon with the baker',\n",
              "       'Salad', 'Chicken Stew', 'Spanish Brunch',\n",
              "       'Raspberry shortbread sandwich', 'Extra Salami or Feta',\n",
              "       'Duck egg', 'Baguette', \"Valentine's card\", 'Tshirt',\n",
              "       'Vegan Feast', 'Postcard', 'Nomad bag', 'Chocolates',\n",
              "       'Coffee granules ', 'Drinking chocolate spoons ',\n",
              "       'Christmas common', 'Argentina Night', 'Half slice Monster ',\n",
              "       'Gift voucher', 'Cherry me Dried fruit', 'Mortimer', 'Raw bars',\n",
              "       'Tacos/Fajita'], dtype=object)"
            ]
          },
          "metadata": {},
          "execution_count": 17
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "df.loc[df[\"Item\"]=='NONE'].shape[0]\n",
        "\n",
        "df = df[df[\"Item\"] != 'NONE']"
      ],
      "metadata": {
        "id": "OqTCZPLCNdVf"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "hot_encoded_df=df.groupby(['Transaction','Item'])['Item'].count().unstack().reset_index().fillna(0).set_index('Transaction')"
      ],
      "metadata": {
        "id": "cRnqf1qCNhEv"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def encode_units(x):\n",
        "    if x <= 0:\n",
        "        return 0\n",
        "    if x >= 1:\n",
        "        return 1\n",
        "hot_encoded_df = hot_encoded_df.applymap(encode_units)"
      ],
      "metadata": {
        "id": "1-Pdh2UNNoxs"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from mlxtend.frequent_patterns import apriori\n",
        "from mlxtend.frequent_patterns import association_rules\n",
        "\n",
        "frequent_itemsets = apriori(hot_encoded_df, min_support=0.01, use_colnames=True)"
      ],
      "metadata": {
        "id": "zetcdRE4Nq9i"
      },
      "execution_count": 21,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "rules = association_rules(frequent_itemsets, metric=\"lift\", min_threshold=1)\n",
        "rules.head(10)"
      ],
      "metadata": {
        "id": "7lnNpOl0NtMD",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 363
        },
        "outputId": "ba6c7bb6-0332-4ad7-e0d1-844069806a39"
      },
      "execution_count": 22,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "       antecedents      consequents  antecedent support  consequent support  \\\n",
              "0      (Alfajores)         (Coffee)            0.036344            0.478394   \n",
              "1         (Coffee)      (Alfajores)            0.478394            0.036344   \n",
              "2          (Bread)         (Pastry)            0.327205            0.086107   \n",
              "3         (Pastry)          (Bread)            0.086107            0.327205   \n",
              "4         (Coffee)        (Brownie)            0.478394            0.040042   \n",
              "5        (Brownie)         (Coffee)            0.040042            0.478394   \n",
              "6           (Cake)         (Coffee)            0.103856            0.478394   \n",
              "7         (Coffee)           (Cake)            0.478394            0.103856   \n",
              "8  (Hot chocolate)           (Cake)            0.058320            0.103856   \n",
              "9           (Cake)  (Hot chocolate)            0.103856            0.058320   \n",
              "\n",
              "    support  confidence      lift  leverage  conviction  \n",
              "0  0.019651    0.540698  1.130235  0.002264    1.135648  \n",
              "1  0.019651    0.041078  1.130235  0.002264    1.004936  \n",
              "2  0.029160    0.089119  1.034977  0.000985    1.003306  \n",
              "3  0.029160    0.338650  1.034977  0.000985    1.017305  \n",
              "4  0.019651    0.041078  1.025860  0.000495    1.001080  \n",
              "5  0.019651    0.490765  1.025860  0.000495    1.024293  \n",
              "6  0.054728    0.526958  1.101515  0.005044    1.102664  \n",
              "7  0.054728    0.114399  1.101515  0.005044    1.011905  \n",
              "8  0.011410    0.195652  1.883874  0.005354    1.114125  \n",
              "9  0.011410    0.109868  1.883874  0.005354    1.057910  "
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
              "      <th>antecedent support</th>\n",
              "      <th>consequent support</th>\n",
              "      <th>support</th>\n",
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              "      <th>conviction</th>\n",
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              "      <th>0</th>\n",
              "      <td>(Alfajores)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.036344</td>\n",
              "      <td>0.478394</td>\n",
              "      <td>0.019651</td>\n",
              "      <td>0.540698</td>\n",
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              "      <td>0.002264</td>\n",
              "      <td>1.135648</td>\n",
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              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>(Alfajores)</td>\n",
              "      <td>0.478394</td>\n",
              "      <td>0.036344</td>\n",
              "      <td>0.019651</td>\n",
              "      <td>0.041078</td>\n",
              "      <td>1.130235</td>\n",
              "      <td>0.002264</td>\n",
              "      <td>1.004936</td>\n",
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              "      <th>2</th>\n",
              "      <td>(Bread)</td>\n",
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              "      <td>0.089119</td>\n",
              "      <td>1.034977</td>\n",
              "      <td>0.000985</td>\n",
              "      <td>1.003306</td>\n",
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              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>(Pastry)</td>\n",
              "      <td>(Bread)</td>\n",
              "      <td>0.086107</td>\n",
              "      <td>0.327205</td>\n",
              "      <td>0.029160</td>\n",
              "      <td>0.338650</td>\n",
              "      <td>1.034977</td>\n",
              "      <td>0.000985</td>\n",
              "      <td>1.017305</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>(Brownie)</td>\n",
              "      <td>0.478394</td>\n",
              "      <td>0.040042</td>\n",
              "      <td>0.019651</td>\n",
              "      <td>0.041078</td>\n",
              "      <td>1.025860</td>\n",
              "      <td>0.000495</td>\n",
              "      <td>1.001080</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>5</th>\n",
              "      <td>(Brownie)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.040042</td>\n",
              "      <td>0.478394</td>\n",
              "      <td>0.019651</td>\n",
              "      <td>0.490765</td>\n",
              "      <td>1.025860</td>\n",
              "      <td>0.000495</td>\n",
              "      <td>1.024293</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(Cake)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.103856</td>\n",
              "      <td>0.478394</td>\n",
              "      <td>0.054728</td>\n",
              "      <td>0.526958</td>\n",
              "      <td>1.101515</td>\n",
              "      <td>0.005044</td>\n",
              "      <td>1.102664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>7</th>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>(Cake)</td>\n",
              "      <td>0.478394</td>\n",
              "      <td>0.103856</td>\n",
              "      <td>0.054728</td>\n",
              "      <td>0.114399</td>\n",
              "      <td>1.101515</td>\n",
              "      <td>0.005044</td>\n",
              "      <td>1.011905</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>8</th>\n",
              "      <td>(Hot chocolate)</td>\n",
              "      <td>(Cake)</td>\n",
              "      <td>0.058320</td>\n",
              "      <td>0.103856</td>\n",
              "      <td>0.011410</td>\n",
              "      <td>0.195652</td>\n",
              "      <td>1.883874</td>\n",
              "      <td>0.005354</td>\n",
              "      <td>1.114125</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>9</th>\n",
              "      <td>(Cake)</td>\n",
              "      <td>(Hot chocolate)</td>\n",
              "      <td>0.103856</td>\n",
              "      <td>0.058320</td>\n",
              "      <td>0.011410</td>\n",
              "      <td>0.109868</td>\n",
              "      <td>1.883874</td>\n",
              "      <td>0.005354</td>\n",
              "      <td>1.057910</td>\n",
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            ]
          },
          "metadata": {},
          "execution_count": 22
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "rules[ (rules['lift'] >= 1) &\n",
        "       (rules['confidence'] >= 0.5) ]"
      ],
      "metadata": {
        "id": "-EamjpY3Nvn4",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 394
        },
        "outputId": "9b8bf32d-108c-4b6b-a4bf-5001097e6288"
      },
      "execution_count": 23,
      "outputs": [
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "         antecedents consequents  antecedent support  consequent support  \\\n",
              "0        (Alfajores)    (Coffee)            0.036344            0.478394   \n",
              "6             (Cake)    (Coffee)            0.103856            0.478394   \n",
              "12         (Cookies)    (Coffee)            0.054411            0.478394   \n",
              "14   (Hot chocolate)    (Coffee)            0.058320            0.478394   \n",
              "16           (Juice)    (Coffee)            0.038563            0.478394   \n",
              "18       (Medialuna)    (Coffee)            0.061807            0.478394   \n",
              "23          (Pastry)    (Coffee)            0.086107            0.478394   \n",
              "24        (Sandwich)    (Coffee)            0.071844            0.478394   \n",
              "27           (Scone)    (Coffee)            0.034548            0.478394   \n",
              "28  (Spanish Brunch)    (Coffee)            0.018172            0.478394   \n",
              "30           (Toast)    (Coffee)            0.033597            0.478394   \n",
              "\n",
              "     support  confidence      lift  leverage  conviction  \n",
              "0   0.019651    0.540698  1.130235  0.002264    1.135648  \n",
              "6   0.054728    0.526958  1.101515  0.005044    1.102664  \n",
              "12  0.028209    0.518447  1.083723  0.002179    1.083174  \n",
              "14  0.029583    0.507246  1.060311  0.001683    1.058553  \n",
              "16  0.020602    0.534247  1.116750  0.002154    1.119919  \n",
              "18  0.035182    0.569231  1.189878  0.005614    1.210871  \n",
              "23  0.047544    0.552147  1.154168  0.006351    1.164682  \n",
              "24  0.038246    0.532353  1.112792  0.003877    1.115384  \n",
              "27  0.018067    0.522936  1.093107  0.001539    1.093366  \n",
              "28  0.010882    0.598837  1.251766  0.002189    1.300235  \n",
              "30  0.023666    0.704403  1.472431  0.007593    1.764582  "
            ],
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>antecedents</th>\n",
              "      <th>consequents</th>\n",
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              "      <th>consequent support</th>\n",
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              "      <th>conviction</th>\n",
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              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>(Alfajores)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.036344</td>\n",
              "      <td>0.478394</td>\n",
              "      <td>0.019651</td>\n",
              "      <td>0.540698</td>\n",
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              "      <td>0.002264</td>\n",
              "      <td>1.135648</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>6</th>\n",
              "      <td>(Cake)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.103856</td>\n",
              "      <td>0.478394</td>\n",
              "      <td>0.054728</td>\n",
              "      <td>0.526958</td>\n",
              "      <td>1.101515</td>\n",
              "      <td>0.005044</td>\n",
              "      <td>1.102664</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>12</th>\n",
              "      <td>(Cookies)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.054411</td>\n",
              "      <td>0.478394</td>\n",
              "      <td>0.028209</td>\n",
              "      <td>0.518447</td>\n",
              "      <td>1.083723</td>\n",
              "      <td>0.002179</td>\n",
              "      <td>1.083174</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>14</th>\n",
              "      <td>(Hot chocolate)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.058320</td>\n",
              "      <td>0.478394</td>\n",
              "      <td>0.029583</td>\n",
              "      <td>0.507246</td>\n",
              "      <td>1.060311</td>\n",
              "      <td>0.001683</td>\n",
              "      <td>1.058553</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>16</th>\n",
              "      <td>(Juice)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.038563</td>\n",
              "      <td>0.478394</td>\n",
              "      <td>0.020602</td>\n",
              "      <td>0.534247</td>\n",
              "      <td>1.116750</td>\n",
              "      <td>0.002154</td>\n",
              "      <td>1.119919</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>18</th>\n",
              "      <td>(Medialuna)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.061807</td>\n",
              "      <td>0.478394</td>\n",
              "      <td>0.035182</td>\n",
              "      <td>0.569231</td>\n",
              "      <td>1.189878</td>\n",
              "      <td>0.005614</td>\n",
              "      <td>1.210871</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>23</th>\n",
              "      <td>(Pastry)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.086107</td>\n",
              "      <td>0.478394</td>\n",
              "      <td>0.047544</td>\n",
              "      <td>0.552147</td>\n",
              "      <td>1.154168</td>\n",
              "      <td>0.006351</td>\n",
              "      <td>1.164682</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>24</th>\n",
              "      <td>(Sandwich)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.071844</td>\n",
              "      <td>0.478394</td>\n",
              "      <td>0.038246</td>\n",
              "      <td>0.532353</td>\n",
              "      <td>1.112792</td>\n",
              "      <td>0.003877</td>\n",
              "      <td>1.115384</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>27</th>\n",
              "      <td>(Scone)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.034548</td>\n",
              "      <td>0.478394</td>\n",
              "      <td>0.018067</td>\n",
              "      <td>0.522936</td>\n",
              "      <td>1.093107</td>\n",
              "      <td>0.001539</td>\n",
              "      <td>1.093366</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>28</th>\n",
              "      <td>(Spanish Brunch)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.018172</td>\n",
              "      <td>0.478394</td>\n",
              "      <td>0.010882</td>\n",
              "      <td>0.598837</td>\n",
              "      <td>1.251766</td>\n",
              "      <td>0.002189</td>\n",
              "      <td>1.300235</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>30</th>\n",
              "      <td>(Toast)</td>\n",
              "      <td>(Coffee)</td>\n",
              "      <td>0.033597</td>\n",
              "      <td>0.478394</td>\n",
              "      <td>0.023666</td>\n",
              "      <td>0.704403</td>\n",
              "      <td>1.472431</td>\n",
              "      <td>0.007593</td>\n",
              "      <td>1.764582</td>\n",
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              "</div>\n",
              "      <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-99b7bf80-673e-4c53-835f-b87219b2083a')\"\n",
              "              title=\"Convert this dataframe to an interactive table.\"\n",
              "              style=\"display:none;\">\n",
              "        \n",
              "  <svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "       width=\"24px\">\n",
              "    <path d=\"M0 0h24v24H0V0z\" fill=\"none\"/>\n",
              "    <path d=\"M18.56 5.44l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94zm-11 1L8.5 8.5l.94-2.06 2.06-.94-2.06-.94L8.5 2.5l-.94 2.06-2.06.94zm10 10l.94 2.06.94-2.06 2.06-.94-2.06-.94-.94-2.06-.94 2.06-2.06.94z\"/><path d=\"M17.41 7.96l-1.37-1.37c-.4-.4-.92-.59-1.43-.59-.52 0-1.04.2-1.43.59L10.3 9.45l-7.72 7.72c-.78.78-.78 2.05 0 2.83L4 21.41c.39.39.9.59 1.41.59.51 0 1.02-.2 1.41-.59l7.78-7.78 2.81-2.81c.8-.78.8-2.07 0-2.86zM5.41 20L4 18.59l7.72-7.72 1.47 1.35L5.41 20z\"/>\n",
              "  </svg>\n",
              "      </button>\n",
              "      \n",
              "  <style>\n",
              "    .colab-df-container {\n",
              "      display:flex;\n",
              "      flex-wrap:wrap;\n",
              "      gap: 12px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert {\n",
              "      background-color: #E8F0FE;\n",
              "      border: none;\n",
              "      border-radius: 50%;\n",
              "      cursor: pointer;\n",
              "      display: none;\n",
              "      fill: #1967D2;\n",
              "      height: 32px;\n",
              "      padding: 0 0 0 0;\n",
              "      width: 32px;\n",
              "    }\n",
              "\n",
              "    .colab-df-convert:hover {\n",
              "      background-color: #E2EBFA;\n",
              "      box-shadow: 0px 1px 2px rgba(60, 64, 67, 0.3), 0px 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "      fill: #174EA6;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "      <script>\n",
              "        const buttonEl =\n",
              "          document.querySelector('#df-99b7bf80-673e-4c53-835f-b87219b2083a button.colab-df-convert');\n",
              "        buttonEl.style.display =\n",
              "          google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "        async function convertToInteractive(key) {\n",
              "          const element = document.querySelector('#df-99b7bf80-673e-4c53-835f-b87219b2083a');\n",
              "          const dataTable =\n",
              "            await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                     [key], {});\n",
              "          if (!dataTable) return;\n",
              "\n",
              "          const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "            '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "            + ' to learn more about interactive tables.';\n",
              "          element.innerHTML = '';\n",
              "          dataTable['output_type'] = 'display_data';\n",
              "          await google.colab.output.renderOutput(dataTable, element);\n",
              "          const docLink = document.createElement('div');\n",
              "          docLink.innerHTML = docLinkHtml;\n",
              "          element.appendChild(docLink);\n",
              "        }\n",
              "      </script>\n",
              "    </div>\n",
              "  </div>\n",
              "  "
            ]
          },
          "metadata": {},
          "execution_count": 23
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import networkx as nx\n",
        "\n",
        "fig, ax=plt.subplots(figsize=(10,4))\n",
        "GA=nx.from_pandas_edgelist(rules,source='antecedents',target='consequents')\n",
        "nx.draw(GA,with_labels=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "rEGBae05NyET",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 248
        },
        "outputId": "5f1903fd-8ec9-4485-850c-7b320afd2240"
      },
      "execution_count": 24,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 720x288 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "## Collaborative Filtering"
      ],
      "metadata": {
        "id": "Zzk11VC8sAlU"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/58.png width=\"750px\">"
      ],
      "metadata": {
        "id": "b6ZP7u2hsEVi"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/59.png width=\"750px\">"
      ],
      "metadata": {
        "id": "FR0oPepksHTX"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "<img src=https://github.com/ai-builders/curriculum/raw/main/images/tabular_images/60.png width=\"750px\">"
      ],
      "metadata": {
        "id": "1xWzJ4cIsI3r"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "item_item_matrix = pd.DataFrame(index=hot_encoded_df.columns,columns=hot_encoded_df.columns)"
      ],
      "metadata": {
        "id": "fS65URhaN2Gf"
      },
      "execution_count": 25,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "from scipy.spatial.distance import cosine\n",
        "\n",
        "for i in range(0,len(item_item_matrix.columns)) :\n",
        "    # Loop through the columns for each column\n",
        "    for j in range(0,len(item_item_matrix.columns)) :\n",
        "      # Fill in placeholder with cosine similarities\n",
        "      item_item_matrix.iloc[i,j] = 1 -cosine(hot_encoded_df.iloc[:,i],hot_encoded_df.iloc[:,j])"
      ],
      "metadata": {
        "id": "vJcd_IZiN4Qt"
      },
      "execution_count": 26,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "links = item_item_matrix.rename_axis('related item', \n",
        "                                     axis='columns').stack().reset_index()\n",
        "links.columns = ['item', 'related item','value']"
      ],
      "metadata": {
        "id": "HwzejpBTN60A"
      },
      "execution_count": 27,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "links_filtered=links.loc[ (links['value'] > 0.1) & \n",
        "                         (links['item'] != links['related item']) ]"
      ],
      "metadata": {
        "id": "egDy81vGN9Ng"
      },
      "execution_count": 28,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import matplotlib.pyplot as plt\n",
        "import networkx as nx\n",
        "\n",
        "fig, ax=plt.subplots(figsize=(25,25))\n",
        "GA=nx.from_pandas_edgelist(links_filtered,source='item',target='related item')\n",
        "pos = nx.spring_layout(GA)\n",
        "nx.draw(GA, pos, with_labels=True)\n",
        "plt.show()"
      ],
      "metadata": {
        "id": "ZhubEKAsN_hX",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "outputId": "54a2e65a-40da-4d2d-8321-ddb3c9b4ee5d"
      },
      "execution_count": 29,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1800x1800 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "# Checkpoint ท้ายบท"
      ],
      "metadata": {
        "id": "l-SI3xAIddlm"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "*   ☑️ เข้าใจเทคนิคการวิเคราะห์ Similarity Matching\n",
        "*   ☑️ เข้าใจถึงกระบวนการในการนำ Similarity Matching ไปใช้"
      ],
      "metadata": {
        "id": "FXL_emvydw4K"
      }
    }
  ]
}